Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Jump to bottom

Prefer tflite_runtime for TFLite inference if installed #6406

Merged
merged 3 commits into from
Jan 25, 2022
Merged

Prefer tflite_runtime for TFLite inference if installed #6406

Changes from all commits
Commits
Show all changes
3 commits
Select commit Hold shift + click to select a range
57ffb8e
import tflite_runtime if tensorflow not installed
motokimura Jan 24, 2022
632facf
rename tflite to tfli
motokimura Jan 25, 2022
022093b
Attempt tflite_runtime for all TFLite workflows
glenn-jocher Jan 25, 2022
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
12 changes: 7 additions & 5 deletions models/common.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -374,17 +374,19 @@ def wrap_frozen_graph(gd, inputs, outputs):
graph_def.ParseFromString(open(w, 'rb').read())
frozen_func = wrap_frozen_graph(gd=graph_def, inputs="x:0", outputs="Identity:0")
elif tflite: # https://www.tensorflow.org/lite/guide/python#install_tensorflow_lite_for_python
if 'edgetpu' in w.lower(): # Edge TPU
try:
import tflite_runtime.interpreter as tfl # prefer tflite_runtime if installed
except ImportError:
import tensorflow.lite as tfl
if 'edgetpu' in w.lower(): # Edge TPU https://coral.ai/software/#edgetpu-runtime
LOGGER.info(f'Loading {w} for TensorFlow Lite Edge TPU inference...')
import tflite_runtime.interpreter as tfli # install https://coral.ai/software/#edgetpu-runtime
delegate = {'Linux': 'libedgetpu.so.1',
'Darwin': 'libedgetpu.1.dylib',
'Windows': 'edgetpu.dll'}[platform.system()]
interpreter = tfli.Interpreter(model_path=w, experimental_delegates=[tfli.load_delegate(delegate)])
interpreter = tfl.Interpreter(model_path=w, experimental_delegates=[tfl.load_delegate(delegate)])
else: # Lite
LOGGER.info(f'Loading {w} for TensorFlow Lite inference...')
import tensorflow as tf
interpreter = tf.lite.Interpreter(model_path=w) # load TFLite model
interpreter = tfl.Interpreter(model_path=w) # load TFLite model
interpreter.allocate_tensors() # allocate
input_details = interpreter.get_input_details() # inputs
output_details = interpreter.get_output_details() # outputs
Expand Down